Many online computer systems offer listings of goods and services for sale, rent, and reservation (for simplicity, “booking” generally) that have or are associated with real world locations that have intangible value to the prospective consumer. For example, in a given city, certain neighborhoods and even particular streets are more desirable than others. A consumer factors the location into their decision whether to book a listing. Existing online computer systems that provide bookings rank listings using location, for example, using a radial distance between a given listing and the designated center of a city, or a reference point, such as a tourist attraction, as one consideration in the ranking.
In some scenarios, the rankings computed for listings may overly skew the top ranked listings towards popular locations or locations that have a relatively large number of listings. As a result, in response to a query that specifies a large geographic area, a consumer may be presented with listings that are not geographically diverse, e.g., clustered in a particular location, even though the geographic area includes many other locations. Similarly, in response to a query that specifies a location, a consumer may be presented with listings that are skewed towards a popular location that is different from the location specified in the query.